Optimization of Water Distribution Networks Using Machine Learning Algorithms

Abstract

Efficient management of water distribution networks is critical for urban infrastructure, requiring advanced techniques to address challenges such as leakage detection, pressure regulation, and demand forecasting. This paper explores the application of machine learning algorithms to optimize these networks, offering a comparative analysis of traditional methods and machine learning approaches. By leveraging supervised learning techniques, including regression and classification models, as well as unsupervised methods like clustering and dimensionality reduction, this study aims to enhance network performance. Reinforcement learning and deep learning approaches are investigated for their potential in real-time optimization and predictive modeling. A case study of a medium-sized urban water distribution network demonstrates significant improvements in leakage detection accuracy, pressure regulation efficiency, and demand forecasting precision. Results indicate that machine learning algorithms provide more accurate predictions and effective fault detection compared to conventional methods. The findings suggest that integrating machine learning into water distribution systems can lead to substantial operational and cost benefits. Future research should focus on scaling these methods to larger networks and integrating them into real-time adaptive systems. This study contributes to the growing body of knowledge on applying machine learning to urban water management, highlighting its transformative potential.